Text Classification
Transformers
Safetensors
English
multilingual
xlm-roberta
multi-label-classification
multi-head-classification
disaster-response
humanitarian-aid
social-media
twitter
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use spencercdz/xlm-roberta-sentiment-requests with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spencercdz/xlm-roberta-sentiment-requests with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="spencercdz/xlm-roberta-sentiment-requests")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("spencercdz/xlm-roberta-sentiment-requests") model = AutoModel.from_pretrained("spencercdz/xlm-roberta-sentiment-requests") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 456
Browse files- model.safetensors +1 -1
- training_log.csv +2 -0
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1109972056
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e6f39c74c738cec6ca4c51ee37ec41f4cd379b9090ff4656f7a3bc40d3958e3d
|
| 3 |
size 1109972056
|
training_log.csv
CHANGED
|
@@ -454,3 +454,5 @@ epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second
|
|
| 454 |
453.0,0.3488135539843814,0.7227599900719781,0.1469811499118805,14.6973,175.066,5.511,0.2592304702681695,298074
|
| 455 |
454.0,0.3486340963302517,0.7229082973763825,0.14698781073093414,14.4343,178.255,5.612,0.2592304702681695,298732
|
| 456 |
455.0,0.3479533258831049,0.7228592702903947,0.14697571098804474,14.5291,177.093,5.575,0.25884181888845703,299390
|
|
|
|
|
|
|
|
|
| 454 |
453.0,0.3488135539843814,0.7227599900719781,0.1469811499118805,14.6973,175.066,5.511,0.2592304702681695,298074
|
| 455 |
454.0,0.3486340963302517,0.7229082973763825,0.14698781073093414,14.4343,178.255,5.612,0.2592304702681695,298732
|
| 456 |
455.0,0.3479533258831049,0.7228592702903947,0.14697571098804474,14.5291,177.093,5.575,0.25884181888845703,299390
|
| 457 |
+
456.0,0.3475539889890405,0.7225864123957092,0.1469770222902298,14.4797,177.697,5.594,0.26000777302759426,300048
|
| 458 |
+
457.0,0.3477717716246475,0.7227708033543393,0.14702320098876953,14.3996,178.685,5.625,0.2592304702681695,300706
|