pytext v0.3.1 Release Notes
Release Date: 2020-01-15 // over 4 years ago-
🆕 New features
- Implement SquadQA tensorizer in TorchScript (#1211)
- ➕ Add session data source for df (#1202)
- ⏱ Dynamic Batch Scheduler Implementation (#1200)
- Implement loss aware sparsifier (#1204)
- Ability to Fine-tune XLM-R for NER on CoNLL Datasets (#1201)
- TorchScriptify Tokenizer after training (#1191)
- Linear Layer only blockwise sparsifier (#478)
- ➕ Adding performance graph to pytext models (#1192)
- Enable inference on GPUs by moving tensors to specified device (#472)
- ➕ Add support for learning from soft labels for Squad (MRC) models (#1188)
- Create byte-aware model that can make byte predictions (#468)
- Minimum Trust Lamb (#1186)
- 👍 Allow model to take byte-level input and make byte-level prediction (#1187)
- ⏱ Scheduler with Warmup (#1184)
- ⚡️ Implement LAMB optimizer (#1183)
- ⏱ CyclicLRScheduler (#1157)
- PyText Entity Linking: ELTask and ELMetricReporter (#1165)
🐛 Bug fixes
- ⬆️ Don't upgrade if Tensorizer already given (#504)
- avoid torchscriptify on a ScriptModule (#1214)
- 👉 Make tensorboard robust to NaN and Inf in model params (#1206)
- 🛠 Fix circleCLI Test broken in D19027834 (#1205)
- 🛠 Fix small bug in pytext vocabulary (#401)
- 🛠 Fix CircleCI failure caused by black and regex (#1199)
- 🛠 Fix CircleCI (#1194)
- 🛠 Fix Circle CI Test broken by D18880705 (#1190)
- 🛠 fix weight load for new fairseq checkpoints (#1189)
- 🛠 Fix Heirarchical intent and slot filling demo is broken (#1012) (#1151)
- 🛠 Fix index error in dict embedding when exported to Caffe2 (#1182)
- 🛠 Fix zero loss tensor in SquadOutputLayer (#1181)
- qa fix for ignore_impossible=False
Other
- 🖨 Printing out error's underlying reason (#1227)
- tidy file path in help text for invocation of docnn.json example (#1221)
- ✅ PyText option to disable CUDA when testing. (#1223)
- 👉 make augmented lstm compatible w other lstms (#1224)
- Vocab recursive lookup (#1222)
- 🛠 Fix simple typo: valus -> value (#1219)
- 👌 support using RoundRobin ProcessGroup in Distributed training (#1213)
- 👉 Use PathManager for all I/O (#1198)
- 👉 Make PathManager robust to API changes in fvcore (#1196)
- 👌 Support for TVM training (BERT) (#1210)
- Exit LM task if special token exists in text for ByteTensorizer (#1207)
- Config adapter for pytext XLM (#1172)
- 👉 Use TensorizerImpl for both training and inference for BERT, RoBERTa and XLM tensorizer (#1195)
- Replace gluster paths with local file paths for NLG configs (#1197)
- 👉 Make BERT Classification compatible with TSEs that return Encoded Layers.
- implement BertTensorizerImpl and XLMTensorizerImpl (#1193)
- 🔧 Make is_input field of tensorizer configurable (#474)
- BERTTensorizerBaseImpl to reimplement BERTTensorizerBase to be TorchScriptable (#1163)
- 👌 Improve LogitsWorkflow to handle dumping of raw inputs and multiple output tensors (#683)
- Accumulative blockwise pruning (#1170)
- Patch for UnicodeDecodeError due to BPE. (#1179)
- ➕ Add pre-loaded task as parameter to caffe2 batch prediction API
- 🍎 Specify CFLAGS to install fairseq in MacOS (#1175)
- Resolve dependency conflict by specifying python-dateutil==2.8.0 (#1176)
- Proper training behavior if setting do_eval=False (#1155)
- 👉 Make DeepCNNRepresentation torchscriptable (#453)